Software development usually requires testers to evaluate the quality of a software product in many ways, including any content such as images incorporated in a software build or generated by execution of a software build. Traditional testing of such images may involve verifying that every pixel of a sample image is identical to the corresponding pixel of a baseline image to detect any unintended changes made to a user interface screen during software development. Although functional, this process for identifying changes made to a user interface screen during software development is cumbersome. Moreover, it is very difficult using this process to identify and perform operations needed to correct such unintended changes made to a user interface image.
Another problem with this approach is that it does not account for large numbers of images that may undergo the same or similar sequence of unintended changes from a baseline image across a progression of software builds. Although the addition of new or modified software code to a build may result in numerous images undergoing the same or similar change, the differences for each image may be individually detected, analyzed and corrected. Unfortunately, such a process may fail to leverage the analytic or corrective operations applied to one image for other images with the same or similar change.
What is needed is a way for more efficiently detecting differences in images where numerous images may undergo the same or similar change from a baseline image. Such a system should be able to leverage the analysis and operations applied to one image for other images with the same or similar change.